Master of Science (MS)
Alzheimer's is an irreversible neurodegenerative disorder described by dynamic psychological and memory defalcation. It has been accounted for that the pervasiveness of Alzheimer's is to increase by 4 times in a few years, where one in every 75 people will have this disorder. Hence, there is a critical requirement for the analysis of Alzheimer's at its beginning stage to diminish the difficulty of the overall medical complications. The initial state of Alzheimer’s is called Mild cognitive impairment (MCI), and hence it is a decent target for premature diagnosis and treatment of Alzheimer's. This project focuses on coordinating numerous imaging modalities to identify people in danger for MCI. The current advancement of brain network connectivity analysis has led to the identification of neurological issues at an entire connectivity level, thereby providing a new road to the classification of brain-related diseases. Utilizing neuroimage pattern classification and various machine learning techniques, we endeavor to incorporate information from CONN toolbox and resting-state functional magnetic resonance imaging (rs-fMRI) for refining MCI prediction accuracy.
Anbukkarasu, Meenakshi, "Pattern Analysis and Prediction of Mild Cognitive Impairment Using the Conn Toolbox" (2020). Master's Projects. 936.